The wealth of information that is now available from the sequencing of the human genome holds great promise for understanding the genetically-based mechanisms that contribute to disease. Extracting maximum benefit from genome sequence requires the most complete and accurate annotation possible. The general goal of my research is to develop new probability models for gene prediction based on the maximum entropy principle. A benefit of this work will be improved software tools for annotators to use for automated gene-structure prediction or computer support for manual annotation. A specific goal is to apply the improved tools to gene prediction in the pathogenic fungus cryptococcus neoformans responsible for opportunistic infections in immuno-compromised patients. Some of the predicted genes will be cloned by a collaborator, Dr. Tamara Doering, providing experimental feedback on the efficacy of the predictions and contributing to our understanding of a disease process and, hopefully, to the development of drugs to combat cryptococcus infections.